Detection of low contrast objects in the ocean has many applications including environmental monitoring, locating schools of fish and sunken ships, and aiding search and rescue operations. Furthermore, military applications include countermine measures (CMM) and anti-submarine warfare (ASW). In addition, navigation applications include the detection and avoidance of navigation hazards, and the interpretation of ocean bottom topographies, such as reefs.
Multispectral images taken from above the ocean's surface generally contain background clutter consisting of light reflected from the ocean surface, light scattered in the atmosphere above the ocean, and upwelling from scattering the water column. Relative to the background clutter, the anomalous objects of interest are generally of very low contrast. The challenge is in removing the pervasive background light so as to render anomalous surface or underwater objects more visible.
Most ocean clutter is due to light reflected from the surface. The prior art to filter ocean clutter includes two methods; one based on temporal averaging of multiple monochromatic images, and the second using multispectral imaging. Temporal averaging uses a video camera. The sequence of images is corrected for frame-to-frame perspective, translational, rotational and magnification changes before stacking (integration). The integration approach works because surface light is modulated at the frequency of the surface waves. Integrating over a time period spanning the wave period, or longer, decreases the surface light. Typical ocean waves of order of 100 m can be filtered with 10-second or longer integration. Shorter waves, order of 1 m, can be filtered just as well in as little as 3 seconds. The method is effective in removing only the surface reflection. It does not eliminate the upwelling light clutter.
The prior art multispectral method makes a global estimate of the background light spectrum and subtracts the estimate pixel-by-pixel. This method implicitly assumes that the background spectrum can be described by a global 1-component spectrum. Prior art as depicted in FIGS. 1A and 1B illustrats multispectral imaging system configurations. The systems use data collected by multispectral imaging systems flown over the ocean, on satellites, (FIG. 1A) or aircraft (FIG. 1B). They work in the daytime, using sunlight to illuminate the object of interest. The imaging camera is usually staring down (nadir view). Typically, the camera is a high quality CCD imaging camera that simultaneously images in several (typically 2 to 10) spectral bands (multispectral), or up to hundreds of spectral bands (hyperspectral).
Thus, there have generally been two different methods utilized for removing the unwanted light reflection in an ocean image. One exploits multiple spectral bands, and the other uses time integration. The choice of which is used leads to different sensor designs--one is a multispectral or hyperspectral imager; the other is simply a long exposure or video camera (possibly with a select narrow spectral band filter).
However, a 1-componenet model does not accurately capture the spectral variability of the clutter because the light in each image pixel is actually a mixture of several components, each having a different spectrum, in relative amounts that can vary from one pixel to another. It is highly desirable to have a process that represents the reflected light more accurately than is possible utilizing a 1-component process. The desired process would subtract much more light clutter, thus making fainter objects easier to detect. In addition, the desired process should allow faster processing of the multispectral image data.